This invention relates to the field of scheduled system maintenance and, in particular, to a distributed processing system for the prediction of remaining lifetimes of system components
Routine maintenance of a system, whether it be a mechanical, electromechanical or chemical system, typically includes the periodic replacement of worn system components. A problem in routine maintenance of such a system is scheduling the optimal replacement time of each component. If maintenance personnel replace a component prematurely, the remaining lifetime of that component is wasted. On the other hand, if maintenance personnel wait to replace a component until after it has failed, the consequences may be catastrophic.
Because the consequence of waiting for failure to occur before replacing a component can be so severe, and because accurate prediction of component lifetime is difficult, maintenance schedulers typically err on the side of premature replacement. This conservative policy increases costs, both because premature replacement results in discarding the remaining lifetime of the replaced component and because premature replacement may result in frequent system shut-downs for each replacement operation.
A significant cause of the difficulties inherent in estimating the useful lifetime of a component is that random disturbances unpredictably shorten the component""s lifetime. For example, one may know the probability that a reactor vessel will fail after repeated exposure to an excessive pressure integrated over an excessive time. However, in the absence of a way to quantitatively integrate the exposure of that vessel to excess pressure, this knowledge is of limited practical value.
In the foregoing example, even if one could monitor a quantity representative of the wear experienced by that component, one would still have to predict the component lifetime on the basis of the values of that quantity. This typically includes the step of transmitting a considerable amount of data from the component to a centralized processor located remotely from the component. This centralized processor is typically also monitoring other indicia of wear associated with other components of the system in an effort to predict remaining lifetimes for those other components, all of which are likewise transmitting considerable amounts of data to the processor. For the sake of economy, this data generally travels on a communication channel shared by all system components. The resulting heavy data traffic generated by data transfer between the various system components and a centralized processor over this shared communication channel interferes with communication between that processor and those system components, thereby militating against the use of shared communication channels. As a result, a dedicated data transmission channel is often provided between each component and the central processor.
Such a dedicated data transmission channel for each component adds cost and leads to inefficiency in the utilization of resources. For example, a first channel between the central processor and a reactant vessel may be exceptionally busy while a second channel between the central processor and a pump may stand idle because of an absence of data traffic between the central processor and the pump. Since these two channels are separate and dedicated to their respective functions, it is not possible to utilize the excess capacity of the second channel to communicate data between the central processor and the reactor vessel.
An additional disadvantage of a lifetime-prediction system as described above is the risk associated with reliance on a centralized processor and data storage facility for storage and processing of data for a multiplicity of components. In such a system, a failure in the central storage facility potentially results in loss of all historical data for all components of the system. Similarly, a failure in the centralized processor can potentially cripple the system by making it virtually impossible to schedule maintenance for all system components.
The disadvantage of reliance on a centralized processor and data storage facility extends beyond the possibility of data loss resulting from equipment failure. For example, if a system component in a first chemical processing plant fails, it may be necessary to replace it with a similar component from a currently idle second processing plant. That second processing plant may be at a remote location and under the control of a different centralized processor having a different data storage facility. As a result, the history of that replacement component may not be readily available to maintenance personnel at the first processing plant.
Similarly, if a component is returned to the manufacturer for routine service, the history of that component may not be readily available to the manufacturer. This will, in turn, hinder the manufacturer""s efforts at servicing the component. As a result, the manufacturer may erroneously perform unneeded service and neglect to perform necessary maintenance tasks.
It is apparent that even a modest number of components can impose an intolerable processing burden on the centralized processor and shared data-communication network of the prior art system described above. In such a system, the centralized processor receives data from many components, all of which are likely to transmit asynchronously. The centralized processor must then process the data to predict the remaining lifetime of each component. If the centralized processor is busy predicting the remaining lifetime of one component, it must find a buffer for temporary storage of the data arriving from another component. At the very least, this results in delays because a large number of components are competing for the limited computational resources of the centralized processor. In some cases, because of the complexity of the software required to manage the competing components, the system may be subject to sporadic failures caused by software bugs. Moreover, an increase in the number of components whose lifetimes are to be predicted further exacerbates these problems.
It is known in the art to perform rudimentary data processing local to a measurement sensor. For example, upon request by an interrogating agent, a dedicated microprocessor located at a sensor may average several instantaneous sensor measurements and transmit an average measurement to a centralized processor. However, in such systems, the locally performed processing steps are not under the control of the interrogating agent. Thus, an interrogating agent that needs a short term average taken over ten measured values may not receive an appropriate response from a dedicated microprocessor pre-programmed to return a long term average over one hundred measured values. In effect, the dedicated microprocessor gathers a set of data, performs the specified calculation, and discards the data, even though additional processing of the same data may yield valuable information in the future, either to the same interrogating agent or to a different interrogating agent.
It is often the case that different interrogating agents may desire different calculations using the same data. For example, in the case of a reactor vessel in which a sensor measures temperature, one interrogating agent may require the amount of time that the temperature is in excess of a particular threshold temperature after initiation of a chemical reaction in order to determine whether the reactants have been consumed. A different interrogating agent may require the number of times since the reactor vessel was last replaced that the temperature has exceeded a second threshold in order to evaluate the continued integrity of the reactor vessel. In both cases, the interrogating agents require access to the same data, but for different calculations.
It is therefore desirable in the art to reduce data traffic between a plurality of components being monitored and the centralized processor monitoring those components and to make that data available for processing by a lifetime-prediction algorithm selected in response to the type of interrogating agent requesting information concerning the system component.
It is also desirable in the art to provide a system in which the computational burden on a single centralized processor remains relatively stable even as the number of components increases.
In a lifetime-prediction system according to the invention, a system component whose remaining lifetime is to be estimated generates a time-varying physical parameter the value of which is indicative of the wear or stress inflicted on that system component. A measuring element then monitors this parameter, by way of a local link or communication channel, and generates a corresponding measured-variable signal.
In response to a request from a remote interrogating agent, the measuring element provides the measured-variable signal to a processor local to the system component, again by means of a local communication channel. This processor predicts, on the basis of the measured-variable signal, the remaining lifetime of the system component. The processor then generates an output signal containing information about the remaining lifetime of the system component and transmits that output signal, through a network interface, to the remote interrogating agent.
Because the monitoring of the measured-variable signal and the estimation of the remaining lifetime are both local to the system component, a system according to the invention reduces data traffic on a network linking several system components to a centralized processor. An additional advantage of the decentralized processing system of the invention is the increased portability of the system component. In a lifetime-prediction system according to the invention, one can remove and replace system components without the need to change maintenance records at a centralized processor because the maintenance data associated with a system component travels with that component.
In a preferred embodiment, the processor generates a remaining lifetime estimate on the basis of the extent to which the measured-variable signal is in excess of a stress threshold. The processor does so by generating a cumulative stress in which each term of a summation corresponds to the extent to which the measured-variable signal exceeds the stress threshold and in which each such term is weighted by the amount of time during which the measured-variable signal exceeds the threshold. Depending on the specific application, the summation can be a sum of linear terms in this extent or, the sum of nth order terms in the extent, or a sum of nth order polynomials in the extent.
The processor, in the preferred embodiment, evaluates a stress rate by dividing the cumulative stress, as evaluated above, by the amount of time during which the measured-variable signal exceeds the stress threshold. The difference between the cumulative stress and a maximum allowable accumulated stress value, when divided by this stress rate, provides an estimate of the remaining lifetime for the system component.
The stress threshold and the maximum allowable stress value in the preferred embodiment are programmable. They can thus be altered by the remote interrogating agent. As a result, a system according to the preferred embodiment of the invention has the capability of using the same data to provide different estimates of the remaining lifetime depending on the purpose of the estimate. Both the stress thresholds and maximum allowable stress values are preferably stored in non-volatile registers to ensure that they are preserved in the event of a power interruption. The ability to re-use the same data for different purposes provides a significant savings in storage requirements for a system embodying the invention.
A system embodying the invention optionally includes an internal clock and access to an external time standard. This optional feature allows the system to resynchronize its internal clock and to compensate for such anomalies as power interruptions, drift in the internal clock, sampling intervals of incorrect duration, and sampling events that occur at incorrect times.